Dataset

This dataset was created by Monis Ahmad

Contents

This dataset contains 55,000 entries of synthetic customer transactions, generated using Python's Faker library. The goal behind creating this dataset was to provide a resource for learners like myself to explore, analyze, and apply various data analysis techniques in a context that closely mimics real-world data.

About the Dataset: - CID (Customer ID): A unique identifier for each customer. - TID (Transaction ID): A unique identifier for each transaction. - Gender: The gender of the customer, categorized as Male or Female. - Age Group: Age group of the customer, divided into several ranges. - Purchase Date: The timestamp of when the transaction took place. - Product Category: The category of the product purchased, such as Electronics, Apparel, etc. - Discount Availed: Indicates whether the customer availed any discount (Yes/No). - Discount Name: Name of the discount applied (e.g., FESTIVE50). - Discount Amount (INR): The amount of discount availed by the customer. - Gross Amount: The total amount before applying any discount. - Net Amount: The final amount after applying the discount. - Purchase Method: The payment method used (e.g., Credit Card, Debit Card, etc.). - Location: The city where the purchase took place.

Use Cases: 1. Exploratory Data Analysis (EDA): This dataset is ideal for conducting EDA, allowing users to practice techniques such as summary statistics, visualizations, and identifying patterns within the data. 2. Data Preprocessing and Cleaning: Learners can work on handling missing data, encoding categorical variables, and normalizing numerical values to prepare the dataset for analysis. 3. Data Visualization: Use tools like Python’s Matplotlib, Seaborn, or Power BI to visualize purchasing trends, customer demographics, or the impact of discounts on purchase amounts. 4. Machine Learning Applications: After applying feature engineering, this dataset is suitable for supervised learning models, such as predicting whether a customer will avail a discount or forecasting purchase amounts based on the input features.

This dataset provides an excellent sandbox for honing skills in data analysis, machine learning, and visualization in a structured but flexible manner.

This is not a real dataset. This dataset was generated using Python's Faker library for the sole purpose of learning

Dataset

This dataset was created by Sohail K. Nikouzad

Contents

This is a Weather dataset analysis project where basic libraries, statistics and different functions of python are used for data analysis. Exploratory Data Analysis has been also implemented to gain better insights.

Dataset

This dataset was created by Saubhagya Mishra

Released under MIT

Contents

At least 350k posts are published on X, 510k comments are posted on Facebook, and 66k pictures and videos are shared on Instagram each minute. These large datasets require substantial processing power, even if only a percentage is collected for analysis and research. To face this challenge, data scientists can now use computer clusters deployed on various IaaS and PaaS services in the cloud. However, scientists still have to master the design of distributed algorithms and be familiar with using distributed computing programming frameworks. It is thus essential to generate tools that provide analysis methods to leverage the advantages of computer clusters for processing large amounts of social network text. This paper presents Whistlerlib, a new Python library for conducting exploratory analysis on large text datasets on social networks. Whistlerlib implements distributed versions of various social media, sentiment, and social network analysis methods that can run atop computer clusters. We experimentally demonstrate the scalability of the various Whistlerlib distributed methods when deployed on a public cloud platform. We also present a practical example of the analysis of posts on the social network X about the Mexico City subway to showcase the features of Whistlerlib in scenarios where social network analysis tools are needed to address issues with a social dimension.

Content

This is a transactions data from an Electronics store chain in the US. The data contains 12 CSV files for each month of 2019. The naming convention is as follows: Sales_[MONTH_NAME]_2019 Each file contains anywhere from around 9000 to 26000 rows and 6 columns. The columns are as follows: Order ID, Product, Quantity Ordered, Price Each, Order Date, Purchase Address There are around 186851 data points combining all the 12-month files. There may be null values in some rows.

Inspiration

Keith Galli

Acknowledgements

• Keith Galli's Youtube video - Solving real world data science tasks with Python Pandas!
• Keith Galli's GitHub Repo - Pandas-Data-Science-Tasks

README – Sample Park Analysis## OverviewThis repository contains a Google Colab / Jupyter notebook and accompanying dataset used for analyzing park features and associated metrics. The notebook demonstrates data loading, cleaning, and exploratory analysis of the Hope_Park_original.csv file.## Contents- sample park analysis.ipynb — The main analysis notebook (Colab/Jupyter format)- Hope_Park_original.csv — Source dataset containing park information- README.md — Documentation for the contents and usage## Usage1. Open the notebook in Google Colab or Jupyter.2. Upload the Hope_Park_original.csv file to the working directory (or adjust the file path in the notebook).3. Run each cell sequentially to reproduce the analysis.## RequirementsThe notebook uses standard Python data science libraries:```pythonpandasnumpymatplotlibseaborn

🕰️ Exploratory Data Analysis of Luxury Watch Prices

  Overview

This project analyzes a large dataset of luxury watches to understand which factors influence price.We focus on brand, movement type, case material, size, gender, and production year.All work was done in Python (Pandas, NumPy, Matplotlib/Seaborn) on Google Colab.

  Dataset

Rows: ~172,000
Columns: 14
Unit of observation: one watch listing

Main columns

name – watch/listing title
price – listed… See the full description on the dataset page: https://huggingface.co/datasets/yotam22/watches.

This is a cleaned version of a Netflix movies dataset prepared for exploratory data analysis (EDA). Missing values have been handled, invalid rows removed, and numerical + categorical columns cleaned for analysis using Python and Pandas.

Python scripts with instructions for the extraction and transformation of original datasets; Transformed datasets; Dataset FA/ LCB constraints.

An exploratory analysis of multiple machine learning models for predicting end-of-treatment acute pain intensity and opioid doses (represented as the total morphine equivalent daily dose (MEDD)) and analgesic efficacy in a large scare retrospective cohort of oral cavity and oropharyngeal cancer patients received radiation therapy (RT).

Description: Dive into the world of exceptional cinema with our meticulously curated dataset, "IMDb's Gems Unveiled." This dataset is a result of an extensive data collection effort based on two critical criteria: IMDb ratings exceeding 7 and a substantial number of votes, surpassing 10,000. The outcome? A treasure trove of 4070 movies meticulously selected from IMDb's vast repository.

What sets this dataset apart is its richness and diversity. With more than 20 data points meticulously gathered for each movie, this collection offers a comprehensive insight into each cinematic masterpiece. Our data collection process leveraged the power of Selenium and Pandas modules, ensuring accuracy and reliability.

Cleaning this vast dataset was a meticulous task, combining both Excel and Python for optimum precision. Analysis is powered by Pandas, Matplotlib, and NLTK, enabling to uncover hidden patterns, trends, and themes within the realm of cinema.

Note: The data is collected as of April 2023. Future versions of this analysis include Movie recommendation system Please do connect for any queries, All Love, No Hate.

Conducted an in-depth analysis of Cyclistic bike-share data to uncover customer usage patterns and trends. Cleaned and processed raw data using Python libraries such as pandas and NumPy to ensure data quality. Performed exploratory data analysis (EDA) to identify insights, including peak usage times, customer demographics, and trip duration patterns. Created visualizations using Matplotlib and Seaborn to effectively communicate findings. Delivered actionable recommendations to enhance customer engagement and optimize operational efficiency.

1. Problem statemont:

This dataset contains data more than 9.5k car sales in Ukraine.Most of then are used car so it open the possibility to analyze featurs related to car operation. This is subset of all car data in Ukraine. Using this we will analyze the various parameter of used car sales in Ukraine.

1.1 Introduction: This Exploratory Data Analysis is to practice python skills till now on a structured dataset including loading, inspecting,wrangling,Exploring and drawing conclusions from the data.The notebook has the obeservations with each step in order to explain throughtly how to approach the dataset. Based on the obseravation some quetions also are answered in the notebook for the reference though not all them are explored in the analysis.

1.2 Data Source and Dataset: a. How was it collected?

Name: Car Sales Sponsering Organization: Dont Know! Year :2019 Description: This is case study of more than 9.5k car sales in Ukraine. b. it is sample? If yes ,What is properly sampled?

Yes .It is sample .We dont have official information about the data collection method, but its appears not to be random sample, so we can assume that it is not representative.

What is Pandas?

Pandas is a Python library used for working with data sets.

It has functions for analyzing, cleaning, exploring, and manipulating data.

The name "Pandas" has a reference to both "Panel Data", and "Python Data Analysis" and was created by Wes McKinney in 2008.

Why Use Pandas?

Pandas allows us to analyze big data and make conclusions based on statistical theories.

Pandas can clean messy data sets, and make them readable and relevant.

Relevant data is very important in data science.

What Can Pandas Do?

Pandas gives you answers about the data. Like:

Is there a correlation between two or more columns?

What is average value?

Max value?

Min value?

Dataset

This dataset was created by Damini Tiwari

Contents

In this project, I have done exploratory data analysis on the UCI Automobile dataset available at https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data

This dataset consists of data From the 1985 Ward's Automotive Yearbook. Here are the sources

1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

Number of Instances: 398 Number of Attributes: 9 including the class attribute

Attribute Information:

mpg: continuous cylinders: multi-valued discrete displacement: continuous horsepower: continuous weight: continuous acceleration: continuous model year: multi-valued discrete origin: multi-valued discrete car name: string (unique for each instance)

This data set consists of three types of entities:

I - The specification of an auto in terms of various characteristics

II - Tts assigned an insurance risk rating. This corresponds to the degree to which the auto is riskier than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is riskier (or less), this symbol is adjusted by moving it up (or down) the scale. Actuaries call this process "symboling".

III - Its normalized losses in use as compared to other cars. This is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/specialty, etc...), and represents the average loss per car per year.

The analysis is divided into two parts:

Data Wrangling

1. Pre-processing data in python
2. Dealing with missing values
3. Data formatting
4. Data normalization
5. Binning

6. Exploratory Data Analysis

7. Descriptive statistics

8. Groupby

9. Analysis of variance

10. Correlation

11. Correlation stats

Acknowledgment Dataset: UCI Machine Learning Repository Data link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data

Startup India - Exploratory Data Analysis

1- The dataset contains updated record of all startups from 1963 to 2021. 2- An Exploratory Data Analysis is performed our the record with different types of data visualizations.

Technologies Used: Python Numpy Pandas Matplotlib Seaborn